Marc-André Nielsen1, Johann Flemming Gloy1, Sabine Bodner2, Emad Maadwad1, Florian Resch3, Jozef Keckes2, Peter Staron1, Martin Müller1
1Institute of Materials Physics, Helmholtz-Zentrum Hereon, Germany
2Erich Schmid Institute, Montanuniversität Leoben, Austria
3Resch GmbH, Glojach, Austria
Additive manufacturing opened up new ways to produce parts with high geometric complexity, e.g., involving internal structures, which led to an increased interest of science and industry in the recent years [1]. Size and shape of the melt pool play an important role in the microstructure formation in materials additively manufactured by laser powder bed fusion (LPBF) techniques. It is an enormous challenge to determine them automatically in radiography image series taken during LPBF when the melt pool has a very low contrast to the surrounding base material [2]. The mechanical behavior and load-bearing capacity of additively manufactured components, however, are still not really understood and subject of intensive research efforts [3]. In particular, residual stresses (RS) play an important role, e.g., for the strength and fatigue properties. Therefore, dynamics of the melt pool and RS distributions were investigated in various parts, fabricated from aluminium alloy powder (AlSi10Mg) using the Laser Powder Bed Fusion (LPBF) technique. The melt pool is detected by a combination of different image processing methods and boundary conditions. The method shown in this presentation is demonstrated on high-speed radiography images taken at the synchrotron beamline 32-ID-B at the Argonne National Lab (ANL/APS) during an in-situ LPBF experiment. Residual stress fields were determined using the high-energy materials science beamline P07 at Deutsches Elektronen Synchrotron (DESY). The diffraction study was carried out in transmission geometry using a photon energy of 87.1 keV that allows penetrating thicker samples. The influence of specimen geometry and production parameters on the RS state will be discussed and the RS state will be discussed in context with the microstructure.
References
[1] Yang, L., et al., Additive manufacturing of metals: the technology, materials, design and production. 2017: Springer.
[2] Nielsen, M. A., Gloy, J. F., Lott, D., Sun, T., Müller, M., & Staron, P. (2022). Automatic melt pool recognition in X-ray radiography images from laser-molten Al alloy. Journal of Materials Research and Technology, 21, 3502-3513.
[3] Campbell, I., et al., Wohlers report 2018: 3D printing and additive manufacturing state of the industry: annual worldwide progress report. 2018: Wohlers Associates.
E-mail of the corresponding author: marc-andre.nielsen@hereon.de